KERANGKA DATA MINING UNTUK MENGOPTIMALKAN SELEKSI PRODUK DALAM DATA SUPERMARKET RETAIL : MODEL PROFSET YANG DIGENERALISASI A DATA MINING FRAMEWORK FOR OPTIMAL PRODUCT SELECTION IN RETAIL SUPERMARKET DATA : THE GENERALIZED PROFSET MODEL

ABSTRAKSI: Teknologi informasi berkembang sangat cepat di luar perkiraan banyak orang. Semakin banyak kemudahan yang dirasakan oleh umat manusia karena perkembangan teknologi informasi. Salah salah satu bidang yang berkembang pesat karena besarnya kebutuhan akan nilai tambah dari database skala besar yang makin banyak terakumulasi sejalan dengan pertumbuhan teknologi informasi adalah data mining. Data mining dapat didefinisikan sebagai serangkaian proses untuk menggali nilai tambah berupa pengetahuan yang selama ini tidak diketahui secara manual dari suatu kumpulan data. Dengan ditunjang kekayaan dan keanekaragaman berbagai bidang ilmu (artificial intelligence, database, statistik, pemodelan matematika, pengolahan citra dsb.) membuat penerapan data mining menjadi makin luas. Salah satu penerapan data mining adalah untuk melakukan seleksi produk di supermarket agar menghasilkan keuntungan maksimum bagi pemiliknya.Pada tugas akhir ini akan diimplementasikan dan dianalisis integrasi pencarian frequent itemset dari association rule dengan sebuah model integer programming untuk seleksi produk (The Generalized PROFSET) pada data retail supermarket. Model PROFSET yang digeneralisasi yaitu model yang mengkombinasikan kriteria atau domain knowledge kualitatif dan kuantitaif dari data retail untuk menentukan set produk yang memberikan keuntungan cross-selling maksimum pada sebuah large basket.Tujuan dari sistem ini adalah untuk membantu para retailer dalam hal mengoptimalisasikan keputusan marketing mix retailnya dari pengadopsian association rule. Diharapkan dengan sistem ini para retailer dapat dengan mudah menetapkan batasan-batasan kategori dari prinsip manajemen dalam pengambilan keputusan marketing-mixnya.Kata Kunci : data mining, association rule, frequent itemset, generalized PROFSET modelABSTRACT: The information technology developes rapidly beyond people’s thought. There are easy things that human kinds feel because of the development of information technology. One of the developing fields accordance with the development of information technology because the huge needs of adding value from big scale data base that are accumlated very much is data mining. It can be defined as the set of processes to get the adding value of science that we haven’t known manually all these times from a set of data . Supported by enrichment and variety of sciences (artificial intelligence. database. statistics, matematics model, image processing, etc), it can make the application of data mining get wide. One of the data mining application is to selection the product in the supermarket so that it can maximum profit for the owner.In the final paper, it can be implementated and anlyzed the searching intergrity of frequent itemset from association rule with a model of integer programming for product selection (The Generalized PROFSET) in retail supermarlet data. The generalized PROFSET model is the model combining between criteria or quality of domain knowledge and quality of retail supermarket data to determine product set that can yield the profit of maximum cross-selling in a large basket.The aim of this system is to help retailers in respect of optimalizing the decision of marketing mix retail from adoption of association rule. Hopely, with this system the retailers can define the category constraints easly from the management principle in making the marketing mix decisionKeyword: data mining, association rule, frequent itemset, generalized PROFSET mode

A Framework for High-Accuracy Privacy-Preserving Mining

To preserve client privacy in the data mining process, a variety of techniques based on random perturbation of data records have been proposed recently. In this paper, we present a generalized matrix-theoretic model of random perturbation, which facilitates a systematic approach to the design of perturbation mechanisms for privacy-preserving mining. Specifically, we demonstrate that (a) the prior techniques differ only in their settings for the model parameters, and (b) through appropriate choice of parameter settings, we can derive new perturbation techniques that provide highly accurate mining results even under strict privacy guarantees. We also propose a novel perturbation mechanism wherein the model parameters are themselves characterized as random variables, and demonstrate that this feature provides significant improvements in privacy at a very marginal cost in accuracy. While our model is valid for random-perturbation-based privacy-preserving mining in general, we specifically evaluate its utility here with regard to frequent-itemset mining on a variety of real datasets. The experimental results indicate that our mechanisms incur substantially lower identity and support errors as compared to the prior techniques

FP-tree and COFI Based Approach for Mining of Multiple Level Association Rules in Large Databases

In recent years, discovery of association rules among itemsets in a large database has been described as an important database-mining problem. The problem of discovering association rules has received considerable research attention and several algorithms for mining frequent itemsets have been developed. Many algorithms have been proposed to discover rules at single concept level. However, mining association rules at multiple concept levels may lead to the discovery of more specific and concrete knowledge from data. The discovery of multiple level association rules is very much useful in many applications. In most of the studies for multiple level association rule mining, the database is scanned repeatedly which affects the efficiency of mining process. In this research paper, a new method for discovering multilevel association rules is proposed. It is based on FP-tree structure and uses cooccurrence frequent item tree to find frequent items in multilevel concept hierarchy.Comment: Pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS, Vol. 7 No. 2, February 2010, USA. ISSN 1947 5500, http://sites.google.com/site/ijcsis

Mining for Useful Association Rules Using the ATMS

Association rule mining has made many achievements in the area of knowledge discovery in databases. Recent years, the quality of the extracted association rules has drawn more and more attention from researchers in data mining community. One big concern is with the size of the extracted rule set. Very often tens of thousands of association rules are extracted among which many are redundant thus useless. In this paper, we first analyze the redundancy problem in association rules and then propose a novel ATMS-based method for extracting non-redundant association rules

Interactive Constrained Association Rule Mining

We investigate ways to support interactive mining sessions, in the setting of association rule mining. In such sessions, users specify conditions (queries) on the associations to be generated. Our approach is a combination of the integration of querying conditions inside the mining phase, and the incremental querying of already generated associations. We present several concrete algorithms and compare their performance.Comment: A preliminary report on this work was presented at the Second International Conference on Knowledge Discovery and Data Mining (DaWaK 2000